Recently I came across an information stating that, if we have too many features, the model is most likely to overfit. I not sure why exactly this is happening. I mean, if I don’t use any higher order polynomial equation, but use a lot of features, will the model still overfit?
Yes. The Modell will still overfit if you use too many features. When you use your modell with many predictors for an out-of-sample prediction (e.g. future outcomes or out-of-sample data) you will likely have an increased prediction error.
You should apply a feature selection algorithm or dimensionality reduction to select the most important features.